Maximum likelihood estimation of probabilistic non-suppression model for OECD NPP electrical fire applying non-negative continuous distribution

The securement and improvement of realism in a probabilistic fire risk assessment (Fire PRA) are important in risk-informed performance-based regulations and decision support. In this study, a probabilistic fire brigade nonsuppression model is developed for electrical fires using the Organization for Economic Cooperation and Development fire incident data on operating nuclear power plants collected from various countries by applying a non-negative continuous probability distribution with the maximum likelihood estimation method. The result of fitting 15 types of a non-negative continuous probability distributions shows that the log-normal probability model is the best fitting and most adequate model, and can best represent the actual fire suppression time by a fire brigade. The selected log-normal probability model was compared with the exponential probability model being used in an existing Fire PRA, which shows that the level of adequacy of the log-normal probability model is improved with a decrease in the bayesian information criteria by 7.9%, residual sum of squares by 100.0%, and mean squared error by 57.6%. The log-normal probability model selected from this study is expected to contribute to an enhancement of the Fire PRA realism in support of risk-informed decision making by reflecting actual fire suppression experience.